CriticalCoEvolution/susceptibility at master · Osrip/CriticalCoEvolution · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
#!/usr/bin/env python

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
from scipy.special import comb
import pickle
from sklearn import manifold
from os import path, makedirs

# beta-1/sim-20180511-163319
# beta-10/sim-20180512-105824
# beta-0-1/sim-20180512-105719
loadfiles = ['beta_experiment/beta-1/sim-20180511-163319',
             'beta_experiment/beta-10/sim-20180512-105824',
             'beta_experiment/beta-0-1/sim-20180512-105719']
# iter_list = [0, 25, 50, 75, 200, 500, 1000]
iter_list = np.arange(0, 4000, 1)
# iter_list = np.arange(0, 2000, 6)
numAgents = 50
# ---- plot settings ------
saveFigBool = True
autoLoad = True
alpha = 0.3

def calc_sus(ising):
    # x = ising.position[0, :] + 4
    # y = ising.position[1, :] + 4

    x = np.diff(ising.position[0, :])
    y = np.diff(ising.position[1, :])

    T = float(len(x))

    xm = np.mean(x)
    ym = np.mean(y)

    x2m = np.mean(x ** 2)
    y2m = np.mean(y ** 2)

    Sx = (x2m - xm ** 2)
    Sy = (y2m - ym ** 2)

    S = Sx + Sy

    return S


for loadfile in loadfiles:
    folder = 'save/' + loadfile
    # fname = folder + \
    #         '/figs/tsne_gen-' + \
    #         str(iter_list[0]) + '-' + str(iter_list[1] - iter_list[0]) + '-' + str(iter_list[-1]) + \
    #         '.png'

    folder2 = folder + '/figs/S/'
    fname2 = folder2 + 'S-' + \
             str(iter_list[0]) + '-' + str(iter_list[1] - iter_list[0]) + '-' + str(iter_list[-1]) + \
             '.npz'

    # See if you've generated this tsne already (they take a long time)
    if path.isfile(fname2) and autoLoad:
        txt = 'Loading: ' + fname2
        print(txt)
        data = np.load(fname2)
        S = data['S']
    else:
        # CALCULUATE T-SNE
        # [ organism(gen),  edges]
        S = np.zeros( ( len(iter_list), numAgents ) )
        iter_label = []

        for iter in iter_list:
            filename = 'save/' + loadfile + '/isings/gen[' + str(iter) + ']-isings.pickle'

            startstr = 'Loading simulation:' + filename
            print(startstr)

            isings = pickle.load(open(filename, 'rb'))
            iter_label.append('Gen: ' + str(iter))

            for orgNum, I in enumerate(isings):
                S[iter, orgNum] = calc_sus(I)


        if not path.exists(folder2):
            makedirs(folder2)

        np.savez(fname2, S=S)


    # PLOT

    fig, ax = plt.subplots(1, 1, figsize=(18, 9))
    fig.text(0.51, 0.035, r'$Generation$', ha='center', fontsize=28)
    fig.text(0.07, 0.5, r'$\chi_{x,y}$', va='center', rotation='vertical', fontsize=28)
    title = r'Positional Susceptibility of Organisms'
    fig.suptitle(title)

    # for orgNum in range(0, numAgents):
    #     ax.scatter(iter_list, S[:, orgNum], color=[0, 0, 0], alpha=alpha)

    # for iter in iter_list:
    #     ax.scatter(iter, np.mean(S[iter, :]), color=[0, 0, 0], alpha=alpha)

    muF = np.mean(S, axis=1)
    ax.scatter(iter_list, np.log10(muF), color=[0, 0, 0], alpha=alpha)
    # ax.set_yscale("log")

    # maxF = np.max(S, axis=1)
    # minF = np.min(S, axis=1)
    # ax.fill_between(iter_list, maxF, minF,
    #                 color=[0.9, 0.9, 0.9])

    # sigmaF = S.std(axis=1)
    # ax.fill_between(iter_list, np.log10(muF + sigmaF, muF - sigmaF),
    #                 color=[0.6, 0.6, 0.6]
    #                 )


    savefilename = fname2[:-4] + '.png'
    if not path.exists(folder2):
        makedirs(folder2)

    if saveFigBool:
        plt.savefig(savefilename, bbox_inches='tight', dpi=150)
        # plt.close()

        savemsg = 'Saving ' + savefilename
        print(savemsg)

plt.show()